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Semantic image segmentation is an essential component of modern autonomous driving systems, as an accurate understanding of the surrounding scene is crucial to navigation and action planning. Current state-of-the-art approaches in semantic image segmentation rely on pre-trained networks that were initially developed for classifying images as a whole. While these networks exhibit outstanding recognition performance (i.e., what is visible?), they lack localization accuracy (i.e., where precisely is something located?). Therefore, additional processing steps have to be performed in order to obtain pixel-accurate segmentation masks at the full image resolution. To alleviate this problem we propose a novel ResNet-like architecture that exhibits strong localization and recognition performance. We combine multi-scale context with pixel-level accuracy by using two processing streams within our network: One stream carries information at the full image resolution, enabling precise adherence to segment boundaries. The other stream undergoes a sequence of pooling operations to obtain robust features for recognition. The two streams are coupled at the full image resolution using residuals. Without additional processing steps and without pre-training, our approach achieves an intersection-over-union score of 71.8% on the Cityscapes dataset.

Tracking in urban street scenes plays a central role in autonomous systems such as self-driving cars. Most of the current vision-based tracking methods perform tracking in the image domain. Other approaches, e.g. based on LIDAR and radar, track purely in 3D. While some vision-based tracking methods invoke 3D information in parts of their pipeline, and some 3D-based methods utilize image-based information in components of their approach, we propose to use image- and world-space information jointly throughout our method. We present our tracking pipeline as a 3D extension of image-based tracking. From enhancing the detections with 3D measurements to the reported positions of every tracked object, we use world- space 3D information at every stage of processing. We accomplish this by our novel coupled 2D-3D Kalman filter, combined with a conceptually clean and extendable hypothesize-and-select framework. Our approach matches the current state-of-the-art on the official KITTI benchmark, which performs evaluation in the 2D image domain only. Further experiments show significant improvements in 3D localization precision by enabling our coupled 2D-3D tracking.

In this paper we propose a novel approach to identify and label the structural elements of furniture e.g. wardrobes, cabinets etc. Given a furniture item, the subdivision into its structural components like doors, drawers and shelves is difficult as the number of components and their spatial arrangements varies severely. Furthermore, structural elements are primarily distinguished by their function rather than by unique color or texture based appearance features. It is therefore difficult to classify them, even if their correct spatial extent were known. In our approach we jointly estimate the number of functional units, their spatial structure, and their corresponding labels by using reversible jump MCMC (rjMCMC), a method well suited for optimization on spaces of varying dimensions (the number of structural elements). Optionally, our system permits to invoke depth information e.g. from RGB-D cameras, which are already frequently mounted on mobile robot platforms. We show a considerable improvement over a baseline method even without using depth data, and an additional performance gain when depth input is enabled.

In this paper, we address the problem of object discovery in time-varying, large-scale image collections. A core part of our approach is a novel Limited Horizon Minimum Spanning Tree (LH-MST) structure that closely approximates the Minimum Spanning Tree at a small fraction of the latter’s computational cost. Our proposed tree structure can be created in a local neighborhood of the matching graph during image retrieval and can be efficiently updated whenever the image database is extended. We show how the LH-MST can be used within both single-link hierarchical agglomerative clustering and the Iconoid Shift framework for object discovery in image collections, resulting in significant efficiency gains and making both approaches capable of incremental clustering with online updates. We evaluate our approach on a dataset of 500k images from the city of Paris and compare its results to the batch version of both clustering algorithms.

Most vision based systems for object tracking in urban environments focus on a limited number of important object categories such as cars or pedestrians, for which powerful detectors are available. However, practical driving scenarios contain many additional objects of interest, for which suitable detectors either do not yet exist or would be cumbersome to obtain. In this paper we propose a more general tracking approach which does not follow the often used tracking-by- detection principle. Instead, we investigate how far we can get by tracking unknown, generic objects in challenging street scenes. As such, we do not restrict ourselves to only tracking the most common categories, but are able to handle a large variety of static and moving objects. We evaluate our approach on the KITTI dataset and show competitive results for the annotated classes, even though we are not restricted to them.

This paper proposes an approach for the semantic seg- mentation and structural parsing of modular furniture items, such as cabinets, wardrobes, and bookshelves, into so called interaction elements. Such a segmentation into functional units is challenging not only due to the visual similarity of the different elements but also because of their often uniformly colored and low-texture appearance. Our method addresses these challenges by merging structural and appearance likelihoods of each element and jointly op- timizing over shape, relative location, and class labels us- ing Markov Chain Monte Carlo (MCMC) sampling. We propose a novel concept called rectangle coverings which provides a tight bound on the number of structural elements and hence narrows down the search space. We evaluate our approach’s performance on a novel dataset of furniture items and demonstrate its applicability in practice.

Many multi-object-tracking (MOT) techniques have been developed over the past years. The most successful ones are based on the classical tracking-by-detection approach. The different methods rely on different kinds of data association, use motion and appearance models, or add optimization terms for occlusion and exclusion. Still, errors occur for all those methods and a consistent evaluation has just started. In this paper we analyze three current state-of-the-art MOT trackers and show that there is still room for improvement. To that end, we train a classifier on the trackers' output bounding boxes in order to prune false positives. Furthermore, the different approaches have different strengths resulting in a reduced false negative rate when combined. We perform an extensive evaluation over ten common evaluation sequences and consistently show improved performances by exploiting the strengths and reducing the weaknesses of current methods.

We present a system to conduct laboratory experiments with thousands of pedestrians. Each participant is equipped with an individual marker to enable us to perform precise tracking and identification. We propose a novel rotation invariant marker design which guarantees a minimal Hamming distance between all used codes. This increases the robustness of pedestrian identification. We present an algorithm to detect these markers, and to track them through a camera network. With our system we are able to capture the movement of the participants in great detail, resulting in precise trajectories for thousands of pedestrians. The acquired data is of great interest in the field of pedestrian dynamics. It can also potentially help to improve multi-target tracking approaches, by allowing better insights into the behaviour of crowds.